Real-time location detection using exclusion zones

ABSTRACT

A system and method for real-time location detection consists of a scalable real time location system (RTLS). It provides revised real time object location determinations. It includes a tag within a location environment, a processor to calculate a location of the tag, and at least one exclusion zone in the environment. Processing includes an original location determination of the tag and a revised location determination of the tag. The revised location determination is calculated by applying attributes of at least one exclusion zone to the original location determination of the tag. Some exclusion zones are defined by no-fly exclusion zones. The revised location determination improves the operation of the RTLS by correcting for impossible and improbable original location determinations. For embodiments, system deployment consists of three phases: collection of training and testing data, network training and testing, and network adaptive maintenance.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/946,979, filed Mar. 3, 2014.

FIELD OF THE INVENTION

The invention relates to real-time wireless object location trackingand, more particularly, to a system and method for object locationdetection employing exclusion zones, where determining the location of atracked object is improved by calculating defined zones in which theobject is unlikely to or cannot exist. Original location determinationsare revised to present a revised location based on exclusion zonecalculations.

BACKGROUND OF THE INVENTION

Real Time Location Systems (RTLSs) track objects, typically byassociated tags. For individuals, a badge is used for tracking inenvironments such as health-care facilities, warehouses, and other areaswhere location is important. Personnel badges and asset tags may includeRadio Frequency Identification (RFID) (passive or active), andcommunicate with fixed or hand-held readers.

While known tags and communication standards may hold the potential forfull-scale deployment (tracking many objects in real-time), in reality,they fall short. Accuracy is impaired by the tracking environment andtime delays from processing bottlenecks when realistic quantities ofobjects are tracked. This leads to stale, inaccurate, object locationindications and even loss of tracking. Solutions are needed to supportthe detection performance needs of actual applications.

Although not related to detection performance, some trackingapplications refer to “exclusion zones”. For example, warning systemsalert authorities when individuals approach or enter forbidden areas. Aparticular definition is: “ . . . an exclusion zone (i.e. a geographicarea that the remote tag 104 is prohibited from entering) . . . ” (U.S.Pat. No. 6,674,368). Some examples of “exclusion zones” refer strictlyto a circular geographic area of a given radius, as for trackingmovements of criminals on parole (U.S. Pat. Nos. 7,864,047, 8,169,316).RadarFind®'s Sentry AV sounds an alarm when a tag approaches a laundryroom or exit to avoid loss of the tag. (“RadarFind Introduces Sentry AVfor RTLS Alarm”, January, 2010). RadarFind® is a registered trademark ofthe RadarFind Corporation of Delaware.

Other applications describe “exclusion zone compliance circuits” thatdisable communications of Global Navigation Satellite System (GNSS)devices when they are in geographic areas such as nations prohibitingsuch devices (U.S. Pat. No. 8,054,181).

In a sports application, helmet-mounted infrared LEDs are tracked. Here,exclusion zones are areas of false data as would be caused by infrared(IR) interference from a light source that might be confused with thehelmet-mounted infrared LEDs. Since the XYZ locations of these sourcesare known, data at these coordinates is not considered and ignored (U.S.2011/0205077).

Finally, animal “exclusion zones” refers to areas around which they areprohibited. These are virtual pens to keep livestock away (U.S. Pat. No.7,719,430).

What is needed is a system and method for improved real-time objectlocation determination that improves detection performance and scaleswith the requirements of the application.

SUMMARY OF THE INVENTION

Embodiments provide a real time location system (RTLS) for revised realtime location determination of at least one object comprising a locationenvironment; at least one tag located within the location environment; aprocessor to calculate a location of the at least one tag located withinthe location environment; at least one exclusion zone in the locationenvironment; an original location determination of the tag in thelocation environment; a revised location determination of the tag in thelocation environment, the revised location determination calculated bythe processor by applying attributes of the at least one exclusion zoneto the original location determination of the at least one tag if it isdetermined that the original location determination is cospatial withthe at least one exclusion zone, and the revised location determinationmodifying operation of the RTLS by modifying original locationdeterminations. For another embodiment, the revised locationdetermination comprises calculation by a neural network by applyingattributes of the at least one exclusion zone to the original locationdetermination of the at least one tag. For some embodiments, thecalculation by the neural network comprises setup and training of theneural network. For another embodiment, the training comprises blurringof the at least one exclusion zone. For continuing embodiments, theblurring comprises Gaussian distribution. For other embodiments, therevised location determination comprises gradient descent. In anotherembodiment, the training comprises diverted output. With anotherembodiment, the at least one exclusion zone comprises locations in whichit would be improbable for tags to be found. In other embodiments, theat least one exclusion zone comprises locations in which it would beimpossible for tags to be found. For embodiments, the original locationdetermination comprises noise. For some embodiments, the originallocation determination comprises at least one missed reading of signalsfrom the at least one tag. For another embodiment, the revised real timelocation determination comprises an input map of the locationenvironment. For further embodiments, the revised real time locationdetermination comprises receiving wireless RF transmissions from the atleast one tag at at least one transceiver.

Other embodiments provide a method for revised real time locationdetermination of at least one object by a real time location system(RTLS) comprising the steps of designating a location environment;obtaining a map of the location environment; defining at least oneexclusion zone in the location environment; providing at least one taglocated within the location environment; obtaining an original locationdetermination; producing a revised location determination of the tag inthe location environment, the revised location determination calculatedby applying attributes of the at least one exclusion zone to theoriginal location determination of the at least one tag, and the revisedlocation determination modifying operation of the RTLS by correcting forimpossible and improbable original location determinations. For anotherembodiment, the step of producing a revised location comprises traininga neural network; and the revised location determination is calculatedby the neural network. In other embodiments, the step of trainingcomprises measured data from the at least one tag. For some embodiments,the measured data comprises at least one physically measurable propertyassociated with a position in three-dimensional space. For anotherembodiment, the step of training comprises modeled data. For furtherembodiments, the method further comprises mathematically defining ano-fly exclusion zone polyhedron; inputting a location estimate;comparing the location estimate with a four dimensional space-timeregion of the no-fly exclusion zone polyhedron; determining if thelocation estimate is within the no fly exclusion zone polyhedron region;locating a boundary of the no-fly exclusion zone polyhedron closest tothe location estimate; defining allowed locations in the closestboundary; revising the estimated location to the defined allowedlocations; and creating a revised virtual path from the estimatedlocation to the revised location.

Further embodiments provide a neural network real time location system(RTLS) for revised real time location determination of at least oneobject comprising a map representing a location environment; at leastone tag located within the location environment; a neural network toprocess a location of the at least one tag located within the locationenvironment, the neural network trained with wireless RF data from theat least one tag and corresponding locations of the at least one tag; atleast one exclusion zone in the location environment, the map processedby Gaussian blurring of the at least one exclusion zone; an originallocation determination of the tag in the location environment; a revisedlocation determination of the tag in the location environment, therevised location determination calculated by the neural network byapplying attributes of the at least one exclusion zone to the originallocation determination of the at least one tag including gradientdescent algorithms, and the revised location determination modifyingoperation of the RTLS by correcting for impossible and improbableoriginal location determinations.

The features and advantages described herein are not all-inclusive and,in particular, many additional features and advantages will be apparentto one of ordinary skill in the art in view of the drawings,specification, and claims. Moreover, it should be noted that thelanguage used in the specification has been principally selected forreadability and instructional purposes, and not to limit the scope ofthe inventive subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified depiction of a portion of an RTLS-configuredbuilding environment in accordance with an embodiment of the invention.

FIG. 2 depicts normal (allowed) and impossible (excluded) zones for anexclusion zone map configured in accordance with an embodiment of theinvention.

FIG. 3 is a depiction of a simplified exclusion zone environmentconfigured in accordance with an embodiment of the invention.

FIG. 4 is a depiction of FIG. 2 Section AA exclusion zone representationconfigured in accordance with an embodiment of the invention.

FIG. 5 is a flowchart of overall operation to produce revised locationdeterminations based on exclusion zones configured in accordance with anembodiment of the invention.

FIG. 6 is a depiction of an exclusion zone ‘energy’ plot configured inaccordance with an embodiment of the invention.

FIG. 7 is a flowchart of neural network training steps configured inaccordance with an embodiment of the invention.

FIG. 8 is a depiction of a neural network exclusion zone implementationconfigured in accordance with an embodiment of the invention.

FIG. 9 is a depiction of an example of exclusion zones' binary imagewith black color denoting (improbable/impossible) excluded zones andwhite color denoting allowed locations configured in accordance with anembodiment of the invention.

FIG. 10 is a flowchart of blurred map generation configured inaccordance with an embodiment of the invention.

FIG. 11 is a flowchart of blurred exclusion zone generation stepsconfigured in accordance with an embodiment of the invention.

FIG. 12 is a depiction of selection and creation of an exclusion zone,and filling with color/intensity gradient configured in accordance withan embodiment of the invention.

FIG. 13 is a depiction of the creation of a smooth exclusion zoneconfigured in accordance with an embodiment of the invention.

FIG. 14 is a depiction of a simplified no-fly exclusion zone revisedlocation configured in accordance with an embodiment of the invention.

FIG. 15 is a depiction of a no-fly exclusion zone virtual pathconfigured in accordance with an embodiment of the invention.

FIG. 16 is a flowchart of a no-fly exclusion zone method configured inaccordance with an embodiment of the invention.

DETAILED DESCRIPTION

Exclusion Zone Operation Methods

Implementation of exclusion zone calculations improves real-time objectlocation determination so that performance scales with the requirementsof the application. Exclusion zone calculations overcome determinationsfor locations that are improbable or impossible. When a tag's originallocation is determined to be associated with an exclusion zone, thelocation is revised based on exclusion zone calculations. As used here,exclusion zones are defined by physical locations, regular or irregular,and/or logical boundaries. Exclusion zones can be related to attributesof the objects associated with tags. For example, exclusion zones can beassociated with people generally, or specific individuals wearing tags.

FIG. 1 presents a simplified example of a portion of a Real TimeLocating System (RTLS)—configured building environment 100. RTLS tags105 are associated with objects/individuals and exist at locationswithin the environment. RTLS Beacons/Routers 110 are located within theenvironment to receive transmissions from RTLS tags 105. While locationsfor tags 105 are typically as expected, at times their determinedlocations can appear to be spurious, or in error. A tag location couldbe improbable 115, such as a location not normal for the tag or theobject with which it is associated. Or, a tag location could beimpossible 120, such as a location within solid columns 125 in which atag could not exist. For example, an improbable tag location 115 can befrom location data from a wrist band tag assigned to a patient thatended up in an equipment closet or clean core—improbable locations. Asexplanation, operating rooms are grouped around a clean core. The cleancore is used for sterile supply storage and is the cleanest area of theoperating suite. Only authorized staff allowed in the clean core.Impossible location examples include inside a structural column or awall, or three feet outside a sixth floor window. Interference or noise130 can cause such spurious determined locations.

FIG. 2 depicts normal (allowed) and impossible (excluded) zones for anexclusion zone map 200 as employed by embodiments. As referenced, tagscannot appear in some areas of the environment in which the system isoperating. These locations are referred to as (impossible) exclusionzones 205 depicted as black. This is in contrast to expected (allowed)locations 210 depicted as white. This is an example of an exclusionzones binary image with black color denoting impossible (excluded) zonesand white color denoting allowed locations.

Exclusion zone operation embodiments define areas/volumes thatcorrespond to spurious tag location readings or undesirable tag locationtranslational movements (where tags should not normally appear such ascutting a corner). Contributors to spurious location determinationinclude noise in the RF system, missed readings by a Beacon/Router, andinterpolated positions between Beacons as can be seen during directionchanges. Such factors can cause calculation errors in determining thelocation, and actually determine a tag to be in the wrong place (insidea wall, for instance). Exclusion zone calculation embodiments defineexcluded areas against which the system checks after an initial locationis calculated. In embodiments, if it is determined that the calculatedlocation is violating an exclusion zone, the system runs anotheralgorithm to start the process of changing/revising the calculatedlocation. Exclusion zones can comprise logical as well as physicaldefinitions and boundaries.

For embodiments, the exclusion zone is blurred, portrayed as atransition from black to white. In embodiments using a Gaussian blur,this transition is Gaussian.

FIG. 3 depicts a simplified exclusion zone environment 300 with solidwalls 305, identifying Section AA through walls of a clean core 310.This horizontal section from the top-down view identifies typical roomareas bounded by solid walls. It would be normal for tags to havelocations in the room areas, but not within the walls.

FIG. 4 depicts a section 400 of a clean core 410 between walls 405 fromFIG. 3, Section AA exclusion zone representation, with a simplifiedGaussian curve distribution superimposed on it 415. This simplifieddepiction illustrates an aspect of the operation of exclusion zones. Inembodiments, a count is kept of the number of times a tag is determinedto have the same location (repetition). The combination of tag locationrepetition and proximity to the smoothed boundary of an exclusion zoneare used to determine a “revised” location. Vertical axis 420 depictsthe number of repetitions (“energy”) required for a tag's revisedlocation to fall within an exclusion zone x (plane) location coordinate425. Outside exclusion zones, the correction is not applied, and a fewor even one/no repetition(s) will result in the revised locationcalculation being equal to the initial location. For improbableexclusion zone locations 430, the number of repetitions would be anintermediate count; for ‘impossible’ exclusion zone locations 435, nonumber of repetitions may be enough to produce a revised location in the‘impossible’ exclusion zone.

FIG. 5 is a high level flow chart 500 of overall operation to producerevised location determinations based on exclusion zones. Initial taglocation is determined 505. Detected location distribution (repetitions)is established 510. The initial location distribution is combined withmodified exclusion zone definitions and or boundaries 515. Locationdeterminations are revised based on the effects of the exclusion zones520.

FIG. 6 presents aspects of exclusion zone gradient descent operationenvisioned as a three dimensional “energy” plot surface 600, where thehigh points (local maxima) are in dark/black 605 and the low points(local minima) are light/white 610. Shades of gray correspond toelevations between the highest (black) and the lowest (white) values. Asmentioned, as location determinations are being made for a tag/object,the number of times an object is determined to be at the same location(repetition) is tracked. The repetition count for a tag's calculatedlocation can be considered to correspond to a probability or ‘energy’associated with that location determination. As an analogy, envision aball placed on the exclusion zone defined surface. A ball placed on thissurface will tend to roll down the surface to the lowest energy state orlowest elevation. The ball can be prevented from ‘rolling’ down thegradient (Gaussian shaped in this example) if it has enough energy. This‘energy’ comes from the repeated location calculations (repetitions)previously mentioned. Therefore, only a few, spurious, miscalculationswill result in the ball ‘rolling’ to the lowest (more probable, orincluded, location) level for the revised location. However,repeated/constant readings in an improbable excluded area will maintainthe tag location in the excluded area even though it is very unlikely tobe there. The previous example for this was of a patient wrist band tagwith an initial location determination in an equipment clean core.Although an improbable location, if for some reason the tag had beenimproperly removed from a patient and placed in a nurse's pocket, andthen entered the equipment clean core, the location could be correct. Inembodiments, many surfaces are created with multiple various transitiongeometries to actually force the ‘ball’ to go to the desired (revised)location. For embodiments, the typical objective is to make the ‘ball’go to the nearest ‘approved’ location. For other embodiments, otherlocations will be the preferred revised location.

Exclusion Zone Neural Network Implementation Method Embodiments

For embodiments, exclusion zone implementation includes neural networks.Generally, the network is trained regarding exclusion zones such that itnever makes a calculation (revised location determination) that locatesan object inside an excluded area. Neural network location determinationis made by inputting data from transmitting tags into the neuralnetwork, which produces an output of the location for each detected tag.Initial setup involves neural net training.

FIG. 7 depicts steps 700 involved in an embodiment of neural networktraining for exclusion zones. Training is accomplished by presenting aplurality of varied input patterns to the neural net, and designatingthe desired result (location). After training, the network's patternrecognition abilities enable mapping real-time input patterns toappropriate output location determinations. Patterns for input arederived 705. For embodiments, two types of training data are used. Onetype is generated data from mathematical models of, for example, indoorelectromagnetic field propagation, and the other type is from actualsampled data inside the facility. Embodiments use either one type or theother type of data, or both types merged and used simultaneously fortraining. A decision is made whether to include mathematical modeledinput data 710. If included, mathematical modeled input data isgenerated 715. A decision is made whether to include measured input data720. If included, measured input data is generated 725. If both typeswere generated 730, they are combined into one data set 735. A singleset of training input data is presented to the neural network 740. Thistraining is essentially presenting the neural network with a multitudeof different patterns for it to remember. In embodiments, each patternassociates some physically measureable property with a position in threedimensional space. Nonlimiting examples include signal strength, time ofarrival, and time of flight of the RF signal. Other nonlimiting examplesinclude infrared and vibration detection of the tag. Once trained, thenetwork is then prepared to process various patterns and output where itthinks the tag is located, based on everything it has been taught. Forembodiments, during training, nonstandard outputs are paired withtraining inputs 745. Standard pairing would involve providing an outputrepresenting the input. As an analogous example, a standard facialrecognition training example would present the network with a collectionof faces and the associated names. Just one picture of a person is notpresented, but many pictures are shown, some with glasses, some withhats, from the side, from below, from above. As many different views areprovided as possible, so that the network can pick the correct nameoutput, even if it is presented with a view never presented in training.In the nonstandard approach of this invention, training pairings aremade with outputs not representing the actual initial location. Theobjective is not to know the name associated with the face, or in thiscase, the initial determined location coordinates of the tag at positionx, y, z. Rather, the objective is to have the network present what itthinks the (revised) x, y, z location is based on ALL of the patternspresented in training. This requires the network to make aninterpretation, and then interpolate/extrapolate a solution based on itsprevious training. In other words, the nonstandard training ‘distorts’the initial location determination to output a revised location. Thisrevised location incorporates ALL input, including ‘diverted’ locationdeterminations used during training.

FIG. 8 illustrates a neural net implementation approach process 800 forexclusion zone training in which the neural network 805 is given inputpatterns for training 810, and has neural net output 815 which cancomprise being provided with a neural net ‘diverted’ output 820. Ineffect, telling it that the answer is something different from theinitial determined location. Therefore, to exclude an area, the neuralnetwork is given the physical measurements (signal strength, forexample) but ‘told’ (training output) that those measurements were takensomeplace other than where they actually were taken. In this way, forembodiments, the network never comes up with a (revised) location answerfor an excluded region. It outputs locations in the areas correspondingto the trained output locations. Essentially, the network's decisionmaking has been ‘pre-biased’ and corrections after-the-fact do not haveto be made.

Each of the after-the-fact blurring (described next) and pre-biasedneural network methods of calculating exclusion zones has its ownapplications. The first method, calculating a revised location after thefact with blurring, works for situations in which it is possible to bein a particular location, even if not probable. The second, pre-biasedneural network method, works well for situations in which it is notpossible for a tag to be in a particular location, such as three feetoutside a window, on the sixth floor, hovering in space.

Exclusion Zone Blurring Method Embodiments

FIG. 9 depicts an example of an exclusion zones binary image with blackcolor denoting excluded zones and white color denoting normal (allowed)locations for an exclusion zone map 900 as employed by embodiments.Certain types of RF tags are not expected to appear in specific areaswhere the system is operating (improbable locations such as a cleancore). In embodiments, those locations are referred to as (improbable)exclusion zones 905. This is in contrast to expected (allowed) locations910. During the neural network training process of the RTLS, if anexclusion zone is present (i.e. if a parameter exclusion map name in amain configuration file is different from ‘none’), waypoints takeninside those (improbable) zones are extracted from the collectedwaypoint data, and the neural network is trained only with the allowedset of points. For embodiments, the map determining the exclusion zonesis a binary portable network graphics (png) format image with blackcolor (grayscale level 0) marking the exclusion zones and white color(grayscale level 255) marking the allowed zones. PNG is a bitmappedimage format that employs lossless data compression. PNG supportspalette-based images (with palettes of 24-bit RGB or 32-bit RGBAcolors), grayscale images (with or without alpha channel), andfull-color non-palette-based RGB(A) images (with or without alphachannel). The png format attributes especially support exclusion zoneprocessing. An example of an exclusion zone setup method follows.

In embodiments, scripts perform the data exclusion. For example, scriptsfor training overlap and test overlap call another function/script toexclude data points that actually performs the data exclusion.Embodiments execute scripts in MATLAB®. MATLAB® is a registeredtrademark of MathWorks, Inc., Corporation in Natick, Mass.

When creating an exclusion zone map as explained in FIGS. 9 & 10, anapproach is to use image processing software. A nonlimiting example isthe GNU Image Manipulation Program (GIMP).

FIG. 10 presents embodiment steps 1000 for the creation of a blurred mapof the facility for exclusion zones. Embodiments begin with a grayscaleimage of the map of the area for RTLS 1005. For images with darkwalls/excluded areas, invert the colors so that the image is mostlyblack, with walls being white 1010. Blur the image with a blurringfilter 1015. Embodiments use a Gaussian Blur for fastest and mostdesired location results. Adjust the size of the blur radius until edgesmoothness is achieved 1020. This stage produces smooth wall edges.Perform a histogram normalization 1025. Perform edge/wall expansion witha dilating filter 1030. Repeat blurring filter 1035 as previously instep 1015. The resulting map 1040 has smooth wall edges and can be usedas the basis for combination with larger exclusion zones. Inembodiments, all exclusion zones are part of a new layer in the overallimage.

FIG. 11 is a flow chart that depicts steps 1100 defining generation ofthe blurred exclusion zone within the previously blurred map. It begins1105 with the blurred map result 1040 from the steps of FIG. 10.Embodiments next create a particular exclusion zone consisting of twolayers by adding a new layer 1110. Define a layer such as ‘exclusionzone binary’ with an attribute of transparency 1115. In this layer, theexclusion zone is formed by selecting the (rectangular or other) area inthe image that will be the area for the exclusion zone 1120 (see FIG.12A). The zone is filled with foreground color (black) 1125. The resultof this operation is shown in FIG. 12B. Next, define a second exclusionzone area layer such as ‘exclusion zone smooth’ 1130. Repeat theexclusion zone selection 1135, also as shown in FIG. 12A. In this zonelayer, apply a blend with a bi-linear shape setting and a color gradientas the filling pattern 1140, as shown in FIG. 13. The result is that onelayer in the image contains black areas of the binary exclusion layer.This is used in embodiments in, for example, MATLAB® software, for dataexclusion. The second layer contains smooth exclusion zones again usedby, for example, MATLAB® scripts to generate data to run a gradientdescent model by system software (the tracking layer service inembodiments). In embodiments, these results are then saved as anexclusion zone smooth binary eXperimental Computing Facility (xcf) file1145. XCF is the native image format of the GIMP image-editing program.It saves all program data handled related to the image, including eachlayer, the current selection, channels, transparency, paths and guides.Next, unlock all layers except Exclusion zone binary layer 1150. Thiscan be done (for example), with the Layers tool. Next, flatten the image1155. The resulting image is a black and white binary exclusion zoneimage to be saved 1160 as a PNG file and processed (as exclusion data)by, for example, MATLAB® train Overlap or test Overlap scripts. Next,the flattened image file (such as Exclusion_zone_smooth_binary.xcf) isopened, and the Exclusion zone_binary layer is unlocked (all otherlayers locked), the Exclusion zone_binary layer image is selected andflattened 1165. Save resulting image as, for example,Exclusion_Zone_smooth.xcf file 1170. Apply one Gaussian blur (as in step1015) 1175 and save image in PNG format 1180. This image is ready to beprocessed by, for example, a MATLAB® generateDerivative.m script.

FIGS. 12A and 12B provide a visual depiction 1200 of steps of theflowchart of FIG. 11. FIG. 12A (top) shows exclusion zone selection andcreation, and FIG. 12 B (bottom) shows filling for color/intensitygradient. Solid walls are shown in white 1205. Open room areas are shownas black 1210. The exclusion zone area for selection is outlined 1215.Filling of selected exclusion zone (FIG. 11, 1125) is illustrated as1220 in FIG. 12B.

FIG. 13 provides a visual depiction 1300 of smoothing steps of theflowchart of FIG. 11. The smoothed exclusion zone is shown with ablack/white gradient 1305.

In embodiments, deployment of exclusion zones requires theimplementation of a gradient descent algorithm and creation ofadditional maps directly based on the floor map of the space where thesystem is installed. Gradient descent is a multivariate optimizationtechnique. It employs an iterative method that, given an initial point,follows the negative of the gradient to move the point toward a criticalpoint, the desired local minimum. This is concerned with localoptimization.

The creation of the data derived from the floor maps is performed, forexample, by running a generateDerivative.m script. For embodiments, thisscript has configuration file named config with the following structure.

TABLE 1 (6) Configuration File Format for Generate Derivative FieldName, Field Value (example) Explanation mask Name, FloorPlan_half_floor1_ The name of the exclusion mapexclusion_smooth_low_res.png used to perform gradient descent search -note that this map has inverted perpendicular axis when compared to themap used in trainOverlap and testOverlap routines. writeFlag, 1 If setto value greater than 0, the script will save derivative in x,derivative in y, map itself and map settings. xp0, 27 Reference point 0x coordinate in pixels (second MATLAB ® coordinate). yp0, 26 Referencepoint 0 y coordinate in pixels (first MATLAB ® coordinate). xprf, 787Reference point 1 x coordinate in pixels (second MATLAB ® coordinate).yprf, 26 Reference point 1 y coordinate in pixels (second MATLAB ®coordinate). D, 85.0 Distance between reference points. test_x, 30 Testpoint x coordinate in feet. test_y, 46 Test point y coordinate in feet.

In embodiments, a script, such as generateDerivative.m, produces allnecessary data to run the gradient descent algorithm that finds localminima in the exclusion zone map—these minima are the allowed locationsof RF tags. The script also performs the test search for the point withcoordinates given through parameters test_x and test_y.

FIG. 14 is a depiction of a simplified no-fly exclusion zone revisedlocation 1400. A no-fly exclusion zone is a region defined by apolyhedron (a solid in three dimensions with flat faces, straight edges,and vertices). Any location estimate that comes up inside a no-flyexclusion zone polyhedron is restricted to the closest boundary of thepolyhedron, similar to hitting a wall. This then will eventually morphinto placing an opening such as a doorway in the boundary, and onlyallow tags to move from one side to the other through the opening. Forembodiments, the opening is defined by an additional intersectingpolyhedron where one polyhedron is a “no-fly”, while the other isdefined as allowable space. In this way a “tunnel” is defined by onepolyhedron such that it penetrates another (no-fly). A nonlimitingexample would be a doorway through a wall, or an entire room with one ormore doorways. In essence, the item associated with the tag will slidealong the wall to the doorway before it can enter a room. This overcomesproblems with slower update rates where the system normally would showmore of an “as the crow flies” movement instead of, for example, downthe hall around the corner and into the room (“as the crow flies” is anidiom for the shortest distance between two points irrespective of theintervening environment). This simplified example shows a no-flyexclusion zone polyhedron 1405 with an original location estimate 1410within no-fly zone polyhedron 1405. Estimated location 1410 is closestto wall face/boundary 1415. In embodiments, polyhedron boundary opening1420 is defined by creating a second polyhedron identified as allowedspace, or it is created by the negative space defined by the originalpolyhedron. For embodiments, no-fly zone polyhedron boundary openingscorrespond to the location of openings designated in facility maps.Location estimate is adjusted with revised location 1425.

FIG. 15 is a depiction of a no-fly exclusion zone virtual path 1500.Similar to FIG. 14, a cross section 1505 of a no-fly exclusion zonepolyhedron is shown. There is a location estimate 1510 (X2) withinno-fly zone polyhedron 1505. Estimated location 1510 is closest topolyhedron wall face/boundary 1515. A polyhedron boundary opening 1520is created by defining another, “tunnel”, polyhedron. Location estimateX2, 1510, is adjusted with revised location 1525 (X′). A virtual path iscreated to replace a misleading “as the crow flies” straight line 1530.This misleading straight line 1530 is a line between PREVIOUS locationestimate 1535 (X1) and location estimate 1510 (X2). X1 location estimate1535 is calculated before location estimate 1510 within no-fly zonepolyhedron 1505. Revised X′ location estimate 1525 is calculated afterlocation estimate 1510 within no-fly zone polyhedron 1505. Misleading“as the crow flies” path 1530 is replaced by virtual path 1545 havingtwo legs. Since it is impossible for the item associated with locationsX1 and X2 to have traveled through the no-fly exclusion zone 1505 bypath 1530, virtual path 1545 through doorway 1520 replaces path 1530.

FIG. 16 is a flowchart depicting steps of a no-fly exclusion zone method1600. Steps comprise: mathematically defining a no-fly exclusion zonepolyhedron 1605; optionally defining a tunnel polyhedron depicting anallowable space region through which objects may pass 1610; inputting alocation estimate 1615; comparing the location estimate with the no-flyexclusion zone polyhedron region 1620; determining if the locationestimate is within the no-fly exclusion zone polyhedron 1625; if not, goto the step of inputting a location estimate 1615; if yes, locate theboundary of the no-fly exclusion zone polyhedron closest to the locationestimate 1630; define an opening (doorway) in closest boundary (or usedefined tunnel polyhedron) 1635; revise the estimated location to thedefined opening location (doorway) 1640; create a revised path from theestimated location to the defined opening location (doorway) 1645. Inembodiments, a line is drawn between the previous location estimate andthe current location estimate. Where the line between the two intersectsthe polyhedron, the location estimate is revised to be at the point ofintersection. In embodiments, to reduce computational time apredetermined list of points is defined such that the processing stepswill revise the location estimate to be that of the closest point to theintersection. This is analogous to dropping a trail of breadcrumbs andchoosing the breadcrumb closest to the intersection with the polyhedron.

The invention has industrial application in the use of electricalcomputing devices and real time object location determination. Theapparatus and methods described allow object location determination bymeans of programming of the computing devices. The types of eventsassociated with the object location determination apparatus andmethodologies include physical and technical phenomena, and thereforehave value in the field of economic endeavor.

The foregoing description of the embodiments of the invention has beenpresented for the purposes of illustration and description. Each andevery page of this submission, and all contents thereon, howevercharacterized, identified, or numbered, is considered a substantive partof this application for all purposes, irrespective of form or placementwithin the application.

This specification is not intended to be exhaustive. Although thepresent application is shown in a limited number of forms, the scope ofthe invention is not limited to just these forms, but is amenable tovarious changes and modifications without departing from the spiritthereof. One or ordinary skill in the art should appreciate afterlearning the teachings related to the claimed subject matter containedin the foregoing description that many modifications and variations arepossible in light of this disclosure. Accordingly, the claimed subjectmatter includes any combination of the above-described elements in allpossible variations thereof, unless otherwise indicated herein orotherwise clearly contradicted by context. In particular, thelimitations presented in dependent claims below can be combined withtheir corresponding independent claims in any number and in any orderwithout departing from the scope of this disclosure, unless thedependent claims are logically incompatible with each other.

What is claimed is:
 1. A real time location system (RTLS) for revisedreal time location determination of at least one object comprising: alocation environment; at least one tag located within said locationenvironment; a processor to calculate a location of said at least onetag located within said location environment; at least one exclusionzone in said location environment; a map representing said locationenvironment, said map processed by Gaussian blurring of said at leastone exclusion zone; an original location determination of said tag insaid location environment; a revised location determination of said tagin said location environment, said revised location determinationcalculated by said processor by applying attributes of said at least oneexclusion zone to said original location determination of said at leastone tag if it is determined that said original location determination iscospatial with said at least one exclusion zone, said revised locationdetermination of said at least one tag including gradient descentalgorithms; and said revised location determination modifying operationof said RTLS by modifying original location determinations.
 2. Thesystem of claim 1, wherein said calculation by said neural networkcomprises setup and training of said neural network.
 3. The system ofclaim 2, wherein said training comprises diverted output.
 4. The systemof claim 1, wherein said at least one exclusion zone comprises locationsin which it would be improbable for tags to be found.
 5. The system ofclaim 1, wherein said at least one exclusion zone comprises locations inwhich it would be impossible for tags to be found.
 6. The system ofclaim 1, wherein said original location determination comprises noise.7. The system of claim 1, wherein said original location determinationcomprises at least one missed reading of signals from said at least onetag.
 8. The system of claim 1, wherein said revised real time locationdetermination comprises an input map of said location environment. 9.The system of claim 1, wherein said revised real time locationdetermination comprises receiving wireless RF transmissions from said atleast one tag at at least one transceiver.
 10. A method for revised realtime location determination of at least one object by a real timelocation system (RTLS) comprising the steps of: designating a locationenvironment; obtaining a map of said location environment; defining atleast one exclusion zone in said location environment , said mapprocessed by Gaussian blurring of said at least one exclusion zone ;providing at least one tag located within said location environment;obtaining an original location determination; producing a revisedlocation determination of said tag in said location environment, saidrevised location determination calculated by applying attributes of saidat least one exclusion zone to said original location determination ofsaid at least one tag, said revised location determination of said atleast one tag including gradient descent algorithms; and said revisedlocation determination modifying operation of said RTLS by correctingfor impossible and improbable original location determinations.
 11. Themethod of claim 10, wherein said step of producing a revised locationcomprises training a neural network; and said revised locationdetermination is calculated by said neural network.
 12. The method ofclaim 11, wherein the step of training comprises measured data from saidat least one tag.
 13. The method of claim 12, wherein said measured datacomprises at least one physically measurable property associated with aposition in three-dimensional space.
 14. The method of claim 11, whereinthe step of training comprises modeled data.
 15. The method of claim 11,further comprising: mathematically defining a no-fly exclusion zonepolyhedron; inputting a location estimate; comparing said locationestimate with a four dimensional space-time region of said no-flyexclusion zone polyhedron; determining if said location estimate iswithin said no fly exclusion zone polyhedron region; locating a boundaryof said no-fly exclusion zone polyhedron closest to said locationestimate; defining allowed locations in said closest boundary; revisingsaid estimated location to said defined allowed locations; and creatinga revised virtual path from said estimated location to said revisedlocation.
 16. A neural network real time location system (RTLS) forrevised real time location determination of at least one objectcomprising: a map representing a location environment; at least one taglocated within said location environment; a neural network to process alocation of said at least one tag located within said locationenvironment, said neural network trained with wireless RF data from saidat least one tag and corresponding locations of said at least one tag;at least one exclusion zone in said location environment, said mapprocessed by Gaussian blurring of said at least one exclusion zone; anoriginal location determination of said tag in said locationenvironment; a revised location determination of said tag in saidlocation environment, said revised location determination calculated bysaid neural network by applying attributes of said at least oneexclusion zone to said original location determination of said at leastone tag including gradient descent algorithms, and said revised locationdetermination modifying operation of said RTLS by correcting forimpossible and improbable original location determinations.